Proceedings Article10.1109/IGARSS.2009.5418070
Optimizing wavelets for hyperspectral image classification
Abdelhamid Daamouche,Farid Melgani,Latifa Hamami +2 more
- 12 Jul 2009
- Vol. 2, pp 302-305
TL;DR: This procedure estimates the coefficients of the wavelet filter bank by means of a particle swarm optimization (PSO) so that to maximize the average Bhattacharyya distance.
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Abstract: This work presents a procedure to optimize a wavelet filter in terms of discrimination capability between the classes characterizing a given hyperspectral remote sensing image To this end, this procedure estimates the coefficients of the wavelet filter bank by means of a particle swarm optimization (PSO) so that to maximize the average Bhattacharyya distance The obtained experimental results show that PSO-based optimized wavelets can significantly outperform conventional wavelets
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Citations
3D multi-resolution wavelet convolutional neural networks for hyperspectral image classification
Cheng Shi,Chi-Man Pun +1 more
TL;DR: 3D MWCNNs model is better at feature representation and approximation from 3D cube data; therefore, they capture the spatial and spectral features more discriminatively to improve the classification accuracy.
47
GA-based design of optimal discrete wavelet filters for efficient wind speed forecasting
TL;DR: This paper suggests a new hybrid scheme WNN, based on discrete wavelet transform (DWT) combined with artificial neural network (ANN) for wind speed forecasting, which outperforms other conventional wavelet-based forecasting structures regarding the wind speed prediction precision.
Wavelet based approaches in optimization theory and practice
Ruchi Agarwal,C. S. Salimath,Khursheed Alam +2 more
- 01 Sep 2017
TL;DR: The article proposes to compile and analyze the comparative performances of various advanced optimization methods using wavelets and the associated transform, highlighting their relative advantages over traditional methods.
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